

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase_   							=  {
    'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig', 'MobileViTOnnxConfig'],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_   							=  ['MobileViTFeatureExtractor']
  lowercase_   							=  ['MobileViTImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_   							=  [
      'MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'MobileViTForImageClassification',
      'MobileViTForSemanticSegmentation',
      'MobileViTModel',
      'MobileViTPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_   							=  [
      'TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFMobileViTForImageClassification',
      'TFMobileViTForSemanticSegmentation',
      'TFMobileViTModel',
      'TFMobileViTPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_mobilevit import MobileViTFeatureExtractor
    from .image_processing_mobilevit import MobileViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilevit import (
        MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileViTForImageClassification,
        MobileViTForSemanticSegmentation,
        MobileViTModel,
        MobileViTPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_mobilevit import (
        TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFMobileViTForImageClassification,
        TFMobileViTForSemanticSegmentation,
        TFMobileViTModel,
        TFMobileViTPreTrainedModel,
    )


else:
  import sys

  lowercase_   							=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int = 10**9)   ->       int:






							'''simple docstring'''





							__UpperCamelCase      :  int       =   1
							__UpperCamelCase      :  Any       =   2

							__UpperCamelCase      :  Dict       =   0
							__UpperCamelCase      :  Any       =   0
							__UpperCamelCase      :  List[Any]       =   0
							while perimeter <= max_perimeter:
														perimeters_sum += perimeter

														prev_value += 2 * value
														value += prev_value

														__UpperCamelCase      :  Optional[int]       =   2 * value + 2 if i % 2 == 0 else 2 * value - 2
														i += 1

							return perimeters_sum


if __name__ == "__main__":
		print(f"{solution() = }")